Dataset bias occurs when collected data overrepresents a particular group or viewpoint. Reviews dominated by one user group may produce insights that do not generalize, and skewed inputs can propagate unfair patterns into AI outputs. section 5.2.1 section 8.5
Use practical checks before trusting conclusions:
- Identify whether reviews predominantly represent one user group, and treat the resulting views as potentially non-representative. section 5.2.1
- Check language coverage and filter non-relevant language data when the analysis expects particular languages. section 5.2.1
- Compare review sentiment with ratings; mismatches can reveal anomalies requiring closer inspection rather than automatic correction. section 5.2.1
- Combine automated quality monitoring with periodic domain-expert review, because automated checks can miss important nuances. section 5.2.1
A narrow review sample
Suppose reviews mainly come from one user group. Their preferences may appear to describe all customers, although the dataset captures only that group’s experience. Treat the result as limited evidence, inspect the raw records, and consider bias before using it for decisions. section 5.2.1 section 8.5
